A useful agent needs more than a prompt: data access, tool permissions, review states, evals, logs, fallbacks and a clear job to do.
PWBy PlugWheel
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The easiest AI agent to build is a chat box. The hardest one is an agent your team trusts with real operational work. The difference is everything around the model.
A production agent has boundaries
It knows what job it owns, what data it can read, what tools it can use, what actions require approval and when it should stop and ask a human. Without boundaries, an agent becomes either dangerous or useless.
The checklist
Grounding: retrieval from trusted docs, records and examples.
Tools: controlled access to CRM, databases, workflows or APIs.
Guardrails: schemas, validation, permissions and human review for sensitive steps.
Evals: real examples that measure whether changes improve or regress quality.
Logs: a visible trail of what the agent did and why.
Fallbacks: defined behavior when the model is unsure or unavailable.
The agent should fit into the system
An agent is rarely the whole product. It is one worker inside a larger automation system: it reads, reasons, drafts, routes or triggers. The dashboard, workflow and review queue are what make it operational.
